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“The Book of Why” Chapters 1&2, a Read with Me series
In my, I kicked off the “Read with Me” book club to explore Judea Pearl’s “The Book of Why”. I would like to thank everyone who has shown interest and to join the club. I am hopeful that we can embark on a journey to deepen our understanding of causality by reading and sharing insights together. After two weeks, as promised, I am sharing some key points I took from the first two chapters.
In these two chapters, Judea starts by explaining the Ladder of Causality and reviews the historical development of causal theory. We will further deep dive into the three Rungs.
Back in 1800, from Galton to Pearson, as they sought to understand how humans inherit genetic traits, they found that correlation was sufficient in a scientific sense. After all, “Data is all there is to science.” To them, causality is merely a special case of correlation that can never be proven. On the other hand, correlation is powerful enough to explain why sons of taller fathers are taller than the population average. Correlation-based forecasting models make predictions by identifying the most predictive variables to the target of interest, even though it might not make sense in many cases. For example, there is a strong correlation between a nation’s per capita chocolate consumption and its number of Nobel Prize winners. Apparently, eating more chocolate wouldn’t give you a higher chance of winning the Nobel Prize, and a country’s wealth is more likely to be the confounder here. We can find a lot of examples like this one that don’t give meaningful and scientific information. When presented with these findings, Pearson dismissed them as mere “spurious” correlations.
Besides “spurious” correlation, it is also common to find correlation found in population reversed in subgroups. For example, when measuring the correlation between skull length and breadth, the correlation is negligible when measured separately in male and female groups. However, it is…
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